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The urban heat island (UHI) phenomenon is a harmful environmental problem in urban areas affecting both climatic and ecological processes. This paper aims to highlight and monitor the spatial distribution of Surface UHI (SUHI) in the Casablanca region, Morocco, using remote sensing data. To achieve this goal, a time series of Landsat TM/ETM+/OLI-TIRS images was acquired from 1984 to 2016 and analyzed. In addition, nocturnal MODIS images acquired from 2005 to 2015 were used to evaluate the nighttime SUHI. In order to better analyze intense heat produced by urban core, SUHI intensity (SUHII) was computed by quantifying the difference of land surface temperature (LST) between urban and rural areas. The urban core SUHII appears more significant in winter seasons than during summer, while the pattern of SUHII becomes moderate during intermediate seasons. During winter, the average daytime SUHII gradually increased in the residential area of Casablanca and in some small peri-urban cities by more than 1 °C from 1984 to 2015. The industrial areas of the Casablanca region were affected by a significant rise in SUHII exceeding 15 °C in certain industrial localities. In contrast, daytime SUHII shows a reciprocal effect during summer with emergence of a heat island in rural areas and development of cool islands in urban and peri-urban areas. During nighttime, the SUHII remains positive in urban areas year-round with higher values in winter as compared to summer. The results point out that the seasonal cycle of daytime SUHII as observed in the Casablanca region is different from other mid-latitude cities, where the highest values are often observed in summer during the day. [ABSTRACT FROM AUTHOR]

An important component of the AVHRR PATMOS-x climate date record (CDR)--or any satellite cloud climatology--is the performance of its cloud detection scheme and the subsequent quality of its cloud fraction CDR. PATMOS-x employs the NOAA Enterprise Cloud Mask for this, which is based on a naïve Bayesian approach. The goal of this paper is to generate analysis of the PATMOS-x cloud fraction CDR to facilitate its use in climate studies. Performance of PATMOS-x cloud detection is compared to that of the well-established MYD35 and CALIPSO products from the EOS A-Train. Results show the AVHRR PATMOS-x CDR compares well against CALIPSO with most regions showing proportional correct values of 0.90 without any spatial filtering and 0.95 when a spatial filter is applied. Values are similar for the NASA MODIS MYD35 mask. A direct comparison of PATMOS-x and MYD35 from 2003 to 2014 also shows agreement over most regions in terms of mean cloud amount, inter-annual variability, and linear trends. Regional and seasonal differences are discussed. The analysis demonstrates that PATMOS-x cloud amount uncertainty could effectively screen regions where PATMOS-x differs from MYD35. [ABSTRACT FROM AUTHOR]

This paper presents a new Fundamental Climate Data Record (FCDR) for the visible (VIS) channel of the Meteosat Visible and Infrared Imager (MVIRI), with pixel-level metrologically traceable uncertainties and error covariance estimates. MVIRI has flown onboard Meteosat First Generation (MFG) satellites between 1982 and 2017. It has served the weather forecasting community with measurements of "visible", "infra-red" and "water vapour" radiance in near real-time. The precision of the pre-launch sensor spectral response function (SRF) characterisation, particularly of the visible band of this sensor type, improved considerably with time, resulting in higher quality radiances towards the end of the MFG program. Despite these improvements, the correction of the degradation of this sensor has remained a challenging task and previous studies have found the SRF degradation to be faster in the blue than in the near-infrared part of the spectrum. With these limitations, the dataset cannot be immediately applied in climate science. In order to provide a data record that is suited for climate studies, the Horizon 2020 project "FIDelity and Uncertainty in Climate-data records from Earth Observation" (FIDUCEO) conducted (1) a thorough metrological uncertainty analysis for each instrument, and (2) a recalibration using enhanced input data such as reconstructed SRFs. In this paper, we present the metrological analysis, the recalibration results and the resulting consolidated FCDR. In the course of this study we were able to trace-back the remaining uncertainties in the calibrated MVIRI reflectances to underlying effects that have distinct physical root-causes and spatial/temporal correlation patterns. SEVIRI and SCIAMACHY reflectances have been used for a validation of the harmonised dataset. The resulting new FCDR is publicly available for climate studies and for the production of climate data records (CDRs) spanning about 35 years. [ABSTRACT FROM AUTHOR]

A long-term archive of cloud properties (cloud top pressure, CTP; and cloud effective emissivity, ε) determined from High-resolution Infrared Radiation Sounder (HIRS) data is investigated for evidence of regional cloud cover change. In the 17 years between 1985 and 2001, different cloud types are analysed over the Australian region (10° S–45° S, 105° E–160° E) and areas of change in total cloud frequency examined. Total cloud frequency change over the Australian region between two adjacent eight-year time periods (1994 to 2001 minus 1985 to 1992) shows the largest increases (ranges between 6% and 12%) of average HIRS total cloud cover occurring over the offshore regions to the northwest and northeast of the continent. Over land, the largest reduction of average HIRS total cloud frequency is in the southwestern region of Australia (between 2% and 8%). Through central Australia, there is a 2% to 7% increase in average HIRS total cloud frequency when comparing these eight-year periods. This paper examines the regional cloud changes in 17 years over Australia that are embedded in global cloud statistics. Examining total HIRS cloud cover frequency over Australia and comparing two different eight-year time periods, has highlighted notable areas of average change. Preliminary reporting of satellite-derived HIRS cloud products and Global Precipitation Climatology Project (GPCP) rainfall products during La Niña seasons between 1985 and 2001 has also been undertaken. [ABSTRACT FROM AUTHOR]

A challenge in addressing climate risk in developing countries is that many regions have extremely limited formal data sets, so for these regions, people must rely on technologies like remote sensing for solutions. However, this means the necessary formal weather data to design and validate remote sensing solutions do not exist. Therefore, many projects use farmers' reported perceptions and recollections of climate risk events, such as drought. However, if these are used to design risk management interventions such as insurance, there may be biases and limitations which could potentially lead to a problematic product. To better understand the value and validity of farmer perceptions, this paper explores two related questions: (1) Is there evidence that farmers reporting data have any information about actual drought events, and (2) is there evidence that it is valuable to address recollection and perception issues when using farmer-reported data? We investigated these questions by analyzing index insurance, in which remote sensing products trigger payments to farmers during loss years. Our case study is perhaps the largest participatory farmer remote sensing insurance project in Ethiopia. We tested the cross-consistency of farmer-reported seasonal vulnerabilities against the years reported as droughts by independent satellite data sources. We found evidence that farmer-reported events are independently reflected in multiple remote sensing datasets, suggesting that there is legitimate information in farmer reporting. Repeated community-based meetings over time and aggregating independent village reports over space lead to improved predictions, suggesting that it may be important to utilize methods to address potential biases. [ABSTRACT FROM AUTHOR]